Analysis of time varying energy period profiles using Hilbert Huang Transform in resting state fMRI for Alzheimer’s disease

Motivation: The time frequency analysis of brain networks in resting state fMRI has largely been based on linear decompositions. Goal(s): The primary goal of this study is to analyze the temporal dynamics of these networks using an adaptive nonlinear approach devoid of any apriori assumptions or basis functions. Approach: Empirical Mode Decomposition (EMD), a data driven technique is utilized to investigate the energy period relationship differences in brain networks across cognitively normal (CN), mild cognitive impairment (MCI) and Alzheimer’s disease (AD). Results: The AD group operates at a higher frequency with reduced energy in typical resting state networks compared to both CN and MCI. Impact: The time varying energy and period profiles obtained from EMD could serve as a potential neuromarker for disease progression from MCI to AD, resulting in timely and early clinical intervention.